PGS Educational Attainment
Import Data and create functions
Create functions
# Calculates cut-off values using mean + 2SD of control (cognitively normal) at baseline
calc_pos_cvalue <- function(variable) {
var_bl <- paste0(deparse(substitute(variable)), "_0")
value <- wide_dat %>%
filter(DX_0 == "CN") %>%
summarize(
{{ variable }} := mean(.[[ var_bl ]], na.rm = TRUE) + 2 * sd(.[[ var_bl ]], na.rm = TRUE)
) %>%
pull({{ variable }}) # Extract the calculated value
return(value)
}
calc_neg_cvalue <- function(variable) {
var_bl <- paste0(deparse(substitute(variable)), "_0")
value <- wide_dat %>%
filter(DX_0 == "CN") %>%
summarize(
{{ variable }} := mean(.[[ var_bl ]], na.rm = TRUE) - 2 * sd(.[[ var_bl ]], na.rm = TRUE)
) %>%
pull({{ variable }}) # Extract the calculated value
return(value)
}
filter_data <- function(outcome_var){
cox_filtered_dat <- cox_filtered_dat %>%
group_by(RID) %>%
mutate(tRecovery = ifelse(lag({{outcome_var}} == 1) & {{outcome_var}} == 0, 1, 0),
tRecovery = ifelse(is.na(tRecovery),0,tRecovery),
ntRecovery = ifelse(any(tRecovery == 1),1,0),)%>%
filter(ntRecovery==0) %>%
relocate({{outcome_var}}, .before=tRecovery)
}
# Fits a Cox Hazards model on a data frame
fit_coxph_PGS <- function(event, filtered_data_frame=cox_filtered_dat){
coxph(Surv(tstart, tstop, get(event)) ~ thirtile_PGS+age+sqrt(age)+PTGENDER+age*PTGENDER, # Polygenic Risk Score EA
data = filtered_data_frame)
}
fit_coxph_years <- function(event, filtered_data_frame=cox_filtered_dat){
coxph(Surv(tstart, tstop, get(event)) ~ thirtile_years+age+sqrt(age)+PTGENDER+age*PTGENDER, # Polygenic Risk Score EA
data = filtered_data_frame)
}
# Plots the survival curve of a Cox Hazards model
plot_coxph_PGS <- function(coxph_model){
pgs_df<- with(cox_filtered_dat,
data.frame(
thirtile_PGS = c("Low PGS","Medium PGS","High PGS"),
age = rep(mean(cox_filtered_dat$age), 3),
PTGENDER = c("Male","Male","Male"))
)
ggsurvplot(
survfit(coxph_model, newdata = pgs_df),
data = cox_filtered_dat,
legend.labs = c("Low PGS","Medium PGS","High PGS"),
ggtheme = theme_minimal() +
theme(
panel.grid.major = element_line(color = "gray"),
panel.grid.minor = element_line(color = "gray", size = 0.2)
),
title = "Survival Curve (~ PGS + AGE + GENDER + AGExGENDER)", # Need to change title
xlab = "Time",
ylab = "Survival Probability",
xlim = c(1.5, 11),
palette = c("#021636", "#bbceed", "#0a77f5"),
conf.int = FALSE # Show confidence intervals
)
}
# Plots the survival curve of a Cox Hazards model
plot_coxph_years <- function(coxph_model){
pgs_df<- with(cox_filtered_dat,
data.frame(
thirtile_years = c("Low EA","Medium EA","High EA"),
age = rep(mean(cox_filtered_dat$age), 3),
PTGENDER = c("Male","Male","Male"))
)
ggsurvplot(
survfit(coxph_model, newdata = pgs_df),
data = cox_filtered_dat,
legend.labs = c("Low EA","Medium EA","High EA"),
ggtheme = theme_minimal() +
theme(
panel.grid.major = element_line(color = "gray"),
panel.grid.minor = element_line(color = "gray", size = 0.2)
),
title = "Survival Curve (~ years + AGE + GENDER + AGExGENDER)", # Need to change title
xlab = "Time",
ylab = "Survival Probability",
xlim = c(1.5, 11),
palette = c("#021636", "#bbceed", "#0a77f5"),
conf.int = FALSE # Show confidence intervals
)
}
# Tests model assumption for the model
test_coxph <- function(coxph_model){
# Testing Proportional Hazards
test.ph <- cox.zph(coxph_model)
test.ph
ggcoxzph(test.ph)
# Testing Outliers & Influential Cases
ggcoxdiagnostics(coxph_model,
type = "dfbeta",
linear.predictions = FALSE,
ggtheme=theme_bw())
}Survival Analysis
Using the ntile function from dplyr, the lower tertile will be assigned value 1 (~ negative residual), middle tertile value 2 and upper tertile value 3 (~positive residual). The time-point is limited to the 9th follow-up (i.e., 48 months).
Mini-Mental State Examination (MMSE)
“The mini–mental state examination (MMSE) is a 30-point questionnaire that is used extensively in clinical and research settings to measure cognitive impairment. It is commonly used in medicine and allied health to screen for dementia. It is also used to estimate the severity and progression of cognitive impairment and to follow the course of cognitive changes in an individual over time; thus making it an effective way to document an individual’s response to treatment.Administration of the test takes between 5 and 10 minutes and examines functions including registration (repeating named prompts), attention and calculation, recall, language, ability to follow simple commands and orientation. […] Any score of 24 or more (out of 30) indicates a normal cognition. Below this, scores can indicate severe (≤9 points), moderate (10–18 points) or mild (19-23 points) cognitive impairment.” (Wikipedia.org). The MMSE scores were normalized using the NormPsy package and then the cut-off was calculated.
Boxplots of MMSE by Age Group at Baseline
To see if it is necessary to stratify for age groups effect of polygenic risk score for EA and age group was tested using linear regression. The results are displayed below.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
##
## Call:
## lm(formula = MMSE ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.415 -1.189 1.109 2.458 3.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.12540 0.16657 162.844 < 2e-16 ***
## EA22 0.45636 0.11384 4.009 6.19e-05 ***
## Age_Group -0.01018 0.06723 -0.151 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.642 on 4822 degrees of freedom
## (6151 observations deleted due to missingness)
## Multiple R-squared: 0.003326, Adjusted R-squared: 0.002912
## F-statistic: 8.045 on 2 and 4822 DF, p-value: 0.0003249##
## Call:
## lm(formula = MMSE_norm ~ EA22 + Age_Group, data = long_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.457 -14.481 1.461 21.439 29.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.4231 0.9389 80.330 < 2e-16 ***
## EA22 3.7284 0.6416 5.811 6.62e-09 ***
## Age_Group -0.1925 0.3790 -0.508 0.611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.53 on 4822 degrees of freedom
## (6151 observations deleted due to missingness)
## Multiple R-squared: 0.006955, Adjusted R-squared: 0.006543
## F-statistic: 16.89 on 2 and 4822 DF, p-value: 4.923e-08MMSE Survival Analysis
Next the survival analysis was conducted for the genetic capacity of educational attainment and the residual educational attainment.
Data Filtering
months <- c("bl", "m03", "m06", "m12", "m18", "m24",
"m30","m36", "m42", "m48", "m54", "m60",
"m66", "m72","m78", "m84", "m90", "m96",
"m102", "m108","m114", "m120", "m126",
"m132", "m144", "m156")
time_points <- data.frame(Time = seq(0,27),
Months = c(0,3,6,12,seq(from = 18, to = 156, by = 6)))
################################################################################
# Filter & Time dependent Age & Calculate tstart and tstop
################################################################################
cox_filtered_dat <- long_dat %>% # filter the data
filter(VISCODE <= 9, AGE >= 60, AGE <= 75) %>%
arrange(RID, VISCODE) %>%
mutate(GENDER = ifelse(PTGENDER== "Male", 0,1), # Calculate numerical Gender variable
VISCODE = VISCODE + 1,
thirtile_PGS = case_when(
thirtile_PGS == 1 ~ "Low PGS",
thirtile_PGS == 2 ~ "Medium PGS",
thirtile_PGS == 3 ~ "High PGS",
TRUE ~ as.character(thirtile_PGS)),
thirtile_years = case_when(
thirtile_years == 1 ~ "Low EA",
thirtile_years == 2 ~ "Medium EA",
thirtile_years == 3 ~ "High EA",
TRUE ~ as.character(thirtile_years)),
age = AGE+(time_points$Months[match(.$VISCODE, time_points$Time)])/12,) %>%
relocate(age, .before=AGE) %>%
group_by(RID) %>%
mutate(tstart = ifelse(VISCODE == 1,0,lag(VISCODE)),
tstop = ifelse(VISCODE != 1, VISCODE,1)) %>%
arrange(RID, VISCODE) %>%
relocate(c(VISCODE,tstart,tstop), .before = thirtile_res)
################################################################################
# Set Variables Types
################################################################################
cox_filtered_dat <- cox_filtered_dat %>%
mutate(thirtile_PGS= as.factor(thirtile_PGS),
thirtile_years= as.factor(thirtile_years),
AGE = as.numeric(AGE),
age = as.numeric(age),
GENDER = as.factor(GENDER),
PTGENDER = as.factor(PTGENDER)) Descriptive Statistics
## [1] 1408## [1] 713print(gender_counts <- cox_filtered_dat %>% # Number of Males/Females in Sample
distinct(RID, .keep_all = TRUE) %>%
group_by(PTGENDER) %>%
summarize(Count = n()))## # A tibble: 2 × 2
## PTGENDER Count
## <fct> <int>
## 1 Male 391
## 2 Female 322print(age_dist <- cox_filtered_dat %>% # Number of Individual by Time-point
group_by(VISCODE) %>%
summarize(Count = n()))## # A tibble: 10 × 2
## VISCODE Count
## <dbl> <int>
## 1 1 713
## 2 2 361
## 3 3 671
## 4 4 611
## 5 5 534
## 6 6 575
## 7 7 331
## 8 8 385
## 9 9 122
## 10 10 334################################################################################
# Set Reference Level of PGS & Years
################################################################################
cox_filtered_dat <- within(cox_filtered_dat,thirtile_PGS <- relevel(thirtile_PGS, ref="Medium PGS")) # Change the Reference Level of the PGS factor
cox_filtered_dat <- within(cox_filtered_dat,thirtile_years <- relevel(thirtile_years, ref="Medium EA")) # Change the Reference Level of the EA factor## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1316
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.18219 0.83344 0.07094 -2.568 0.0102 *
## thirtile_PGSLow PGS 0.08976 1.09391 0.06475 1.386 0.1657
## age -0.45327 0.63554 0.44612 -1.016 0.3096
## sqrt(age) 7.64454 2089.19740 7.45461 1.025 0.3051
## PTGENDERFemale -1.41102 0.24390 1.00274 -1.407 0.1594
## age:PTGENDERFemale 0.01974 1.01994 0.01409 1.401 0.1613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.8334 1.1998456 0.7252484 9.578e-01
## thirtile_PGSLow PGS 1.0939 0.9141538 0.9635286 1.242e+00
## age 0.6355 1.5734545 0.2650981 1.524e+00
## sqrt(age) 2089.1974 0.0004787 0.0009432 4.628e+09
## PTGENDERFemale 0.2439 4.1001219 0.0341716 1.741e+00
## age:PTGENDERFemale 1.0199 0.9804532 0.9921525 1.048e+00
##
## Concordance= 0.541 (se = 0.009 )
## Likelihood ratio test= 22.13 on 6 df, p=0.001
## Wald test = 21.47 on 6 df, p=0.002
## Score (logrank) test = 21.55 on 6 df, p=0.001## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 4637, number of events= 1316
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 6.311e-01 1.880e+00 6.535e-02 9.656 < 2e-16 ***
## thirtile_yearsHigh EA -2.225e-01 8.005e-01 7.540e-02 -2.950 0.00317 **
## age -6.341e-01 5.304e-01 4.422e-01 -1.434 0.15161
## sqrt(age) 1.056e+01 3.873e+04 7.388e+00 1.430 0.15273
## PTGENDERFemale -8.218e-01 4.396e-01 1.005e+00 -0.818 0.41342
## age:PTGENDERFemale 9.659e-03 1.010e+00 1.413e-02 0.684 0.49417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.880e+00 5.320e-01 1.65365 2.136e+00
## thirtile_yearsHigh EA 8.005e-01 1.249e+00 0.69056 9.280e-01
## age 5.304e-01 1.885e+00 0.22296 1.262e+00
## sqrt(age) 3.873e+04 2.582e-05 0.01993 7.528e+10
## PTGENDERFemale 4.397e-01 2.275e+00 0.06136 3.150e+00
## age:PTGENDERFemale 1.010e+00 9.904e-01 0.98213 1.038e+00
##
## Concordance= 0.621 (se = 0.009 )
## Likelihood ratio test= 175.9 on 6 df, p=<2e-16
## Wald test = 179.3 on 6 df, p=<2e-16
## Score (logrank) test = 187.2 on 6 df, p=<2e-16## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the survminer package.
## Please report the issue at <https://github.com/kassambara/survminer/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Cox Assumptions
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
Alzheimer’s Disease Assessment Scale
The Cognitive Subscale Alzheimer’s Disease Assessment Scale (ADAS) is made of 11 tasks that include both subject-completed tests and observer-based assessments, assessing the memory, language, and praxis domains. The result is a global final score ranging from 0 to 70, based on the sum of the scores of the single tasks (ADAS11).
Beyond the ADAS11 score, the ADNI study included also an additional test of delayed word recall and a number cancellation or maze task, which are further summed to have a new total score that ranges from 0 to 85 (ADAS13).
In addition, the score of the task 4 (Word Recognition, ADASQ4) was included in the ADNIMERGE dataset.
ADAS11
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 3393, number of events= 823
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -9.935e-02 9.054e-01 8.781e-02 -1.131 0.258
## thirtile_PGSLow PGS 2.499e-02 1.025e+00 8.367e-02 0.299 0.765
## age 8.615e-01 2.367e+00 5.614e-01 1.535 0.125
## sqrt(age) -1.421e+01 6.746e-07 9.360e+00 -1.518 0.129
## PTGENDERFemale -1.987e+00 1.371e-01 1.228e+00 -1.618 0.106
## age:PTGENDERFemale 2.782e-02 1.028e+00 1.724e-02 1.614 0.107
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 9.054e-01 1.104e+00 7.623e-01 1.075
## thirtile_PGSLow PGS 1.025e+00 9.753e-01 8.702e-01 1.208
## age 2.367e+00 4.225e-01 7.876e-01 7.112
## sqrt(age) 6.746e-07 1.482e+06 7.271e-15 62.592
## PTGENDERFemale 1.371e-01 7.295e+00 1.235e-02 1.521
## age:PTGENDERFemale 1.028e+00 9.726e-01 9.940e-01 1.064
##
## Concordance= 0.548 (se = 0.012 )
## Likelihood ratio test= 13.53 on 6 df, p=0.04
## Wald test = 13.52 on 6 df, p=0.04
## Score (logrank) test = 13.56 on 6 df, p=0.03## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 3393, number of events= 823
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 6.106e-01 1.842e+00 8.428e-02 7.245 4.33e-13 ***
## thirtile_yearsHigh EA -1.831e-01 8.327e-01 9.393e-02 -1.949 0.0513 .
## age 6.715e-01 1.957e+00 5.565e-01 1.207 0.2276
## sqrt(age) -1.115e+01 1.441e-05 9.279e+00 -1.201 0.2296
## PTGENDERFemale -1.141e+00 3.196e-01 1.233e+00 -0.925 0.3548
## age:PTGENDERFemale 1.382e-02 1.014e+00 1.733e-02 0.797 0.4252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.842e+00 5.430e-01 1.561e+00 2.172
## thirtile_yearsHigh EA 8.327e-01 1.201e+00 6.927e-01 1.001
## age 1.957e+00 5.109e-01 6.575e-01 5.826
## sqrt(age) 1.441e-05 6.940e+04 1.823e-13 1139.298
## PTGENDERFemale 3.196e-01 3.129e+00 2.853e-02 3.580
## age:PTGENDERFemale 1.014e+00 9.863e-01 9.801e-01 1.049
##
## Concordance= 0.617 (se = 0.011 )
## Likelihood ratio test= 102.1 on 6 df, p=<2e-16
## Wald test = 104.8 on 6 df, p=<2e-16
## Score (logrank) test = 108.8 on 6 df, p=<2e-16
Cox Assumptions
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
ADAS13
“The ADAS13 was included as a global measure of cognitive function. ADAS13 is a test battery developed to assess severity of cognitive impairment associated with AD and includes subtests and clinical evaluations assessing memory function, reasoning, language function, orientation and praxis. The ADAS13 is a modified version of the original ADAS-Cog-11, adding a cancellation task and a delayed free recall task. The higher the scores, the more severe impairment of cognitive function.” (Mofrad et al., 2021)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 3289, number of events= 807
## (8 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -1.160e-01 8.905e-01 8.838e-02 -1.312 0.1894
## thirtile_PGSLow PGS -8.181e-03 9.919e-01 8.452e-02 -0.097 0.9229
## age 1.393e+00 4.027e+00 5.646e-01 2.467 0.0136 *
## sqrt(age) -2.299e+01 1.035e-10 9.410e+00 -2.443 0.0146 *
## PTGENDERFemale -2.281e+00 1.022e-01 1.235e+00 -1.848 0.0647 .
## age:PTGENDERFemale 3.191e-02 1.032e+00 1.731e-02 1.843 0.0653 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.905e-01 1.123e+00 7.489e-01 1.05890
## thirtile_PGSLow PGS 9.919e-01 1.008e+00 8.404e-01 1.17056
## age 4.027e+00 2.483e-01 1.332e+00 12.17970
## sqrt(age) 1.035e-10 9.658e+09 1.012e-18 0.01059
## PTGENDERFemale 1.022e-01 9.787e+00 9.088e-03 1.14875
## age:PTGENDERFemale 1.032e+00 9.686e-01 9.980e-01 1.06806
##
## Concordance= 0.559 (se = 0.012 )
## Likelihood ratio test= 21.8 on 6 df, p=0.001
## Wald test = 21.91 on 6 df, p=0.001
## Score (logrank) test = 22.02 on 6 df, p=0.001## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 3289, number of events= 807
## (8 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 6.179e-01 1.855e+00 8.513e-02 7.258 3.92e-13 ***
## thirtile_yearsHigh EA -2.232e-01 7.999e-01 9.519e-02 -2.345 0.0190 *
## age 1.181e+00 3.258e+00 5.581e-01 2.116 0.0343 *
## sqrt(age) -1.957e+01 3.160e-09 9.300e+00 -2.105 0.0353 *
## PTGENDERFemale -1.412e+00 2.436e-01 1.239e+00 -1.140 0.2544
## age:PTGENDERFemale 1.739e-02 1.018e+00 1.740e-02 0.999 0.3176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.855e+00 5.391e-01 1.570e+00 2.1918
## thirtile_yearsHigh EA 7.999e-01 1.250e+00 6.638e-01 0.9640
## age 3.258e+00 3.069e-01 1.091e+00 9.7282
## sqrt(age) 3.160e-09 3.165e+08 3.832e-17 0.2605
## PTGENDERFemale 2.436e-01 4.106e+00 2.147e-02 2.7631
## age:PTGENDERFemale 1.018e+00 9.828e-01 9.834e-01 1.0528
##
## Concordance= 0.628 (se = 0.011 )
## Likelihood ratio test= 116.5 on 6 df, p=<2e-16
## Wald test = 118.9 on 6 df, p=<2e-16
## Score (logrank) test = 123.7 on 6 df, p=<2e-16
Cox Assumptions
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
ADASQ4
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2919, number of events= 690
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 5.070e-02 1.052e+00 9.723e-02 0.521 0.60203
## thirtile_PGSLow PGS 1.808e-01 1.198e+00 9.308e-02 1.942 0.05214 .
## age 1.830e+00 6.235e+00 6.021e-01 3.040 0.00237 **
## sqrt(age) -3.044e+01 6.055e-14 1.002e+01 -3.036 0.00239 **
## PTGENDERFemale -3.144e+00 4.310e-02 1.305e+00 -2.410 0.01595 *
## age:PTGENDERFemale 4.397e-02 1.045e+00 1.832e-02 2.400 0.01638 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.052e+00 9.506e-01 8.695e-01 1.273e+00
## thirtile_PGSLow PGS 1.198e+00 8.346e-01 9.983e-01 1.438e+00
## age 6.235e+00 1.604e-01 1.916e+00 2.029e+01
## sqrt(age) 6.055e-14 1.651e+13 1.780e-22 2.060e-05
## PTGENDERFemale 4.310e-02 2.320e+01 3.342e-03 5.558e-01
## age:PTGENDERFemale 1.045e+00 9.570e-01 1.008e+00 1.083e+00
##
## Concordance= 0.565 (se = 0.013 )
## Likelihood ratio test= 25.01 on 6 df, p=3e-04
## Wald test = 25.43 on 6 df, p=3e-04
## Score (logrank) test = 25.59 on 6 df, p=3e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2919, number of events= 690
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 7.327e-01 2.081e+00 9.181e-02 7.981 1.45e-15 ***
## thirtile_yearsHigh EA -3.586e-01 6.987e-01 1.056e-01 -3.395 0.000686 ***
## age 1.606e+00 4.983e+00 5.932e-01 2.707 0.006780 **
## sqrt(age) -2.677e+01 2.377e-12 9.873e+00 -2.711 0.006707 **
## PTGENDERFemale -1.494e+00 2.245e-01 1.316e+00 -1.135 0.256322
## age:PTGENDERFemale 1.771e-02 1.018e+00 1.851e-02 0.957 0.338756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.081e+00 4.806e-01 1.738e+00 2.490923
## thirtile_yearsHigh EA 6.987e-01 1.431e+00 5.680e-01 0.859372
## age 4.983e+00 2.007e-01 1.558e+00 15.934656
## sqrt(age) 2.377e-12 4.207e+11 9.387e-21 0.000602
## PTGENDERFemale 2.245e-01 4.454e+00 1.702e-02 2.960896
## age:PTGENDERFemale 1.018e+00 9.824e-01 9.816e-01 1.055469
##
## Concordance= 0.656 (se = 0.012 )
## Likelihood ratio test= 152.2 on 6 df, p=<2e-16
## Wald test = 153.8 on 6 df, p=<2e-16
## Score (logrank) test = 163.3 on 6 df, p=<2e-16
Cox Assumptions
## Warning: arguments formula is deprecated; will be removed in the next version;
## please use fit instead.
CDRSB
“The clinical dementia rating (CDR) scale is commonly used to diagnose dementia due to Alzheimer’s disease (AD). The sum of boxes of the CDR (CDR-SB) has recently been emphasized and applied to interventional trials for tracing the progression of cognitive impairment (CI) in the early stages of AD.” (Tzeng et al., 2022)
See Table 3 for explanation on the staging category (O’Bryant et al., 2012)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2228, number of events= 1376
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.282e-01 1.137e+00 6.830e-02 1.877 0.06047 .
## thirtile_PGSLow PGS 1.966e-01 1.217e+00 6.738e-02 2.918 0.00352 **
## age 2.300e+00 9.977e+00 3.850e-01 5.975 2.30e-09 ***
## sqrt(age) -3.912e+01 1.023e-17 6.405e+00 -6.108 1.01e-09 ***
## PTGENDERFemale 9.272e-01 2.527e+00 8.560e-01 1.083 0.27873
## age:PTGENDERFemale -1.396e-02 9.861e-01 1.214e-02 -1.150 0.25033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.137e+00 8.797e-01 9.944e-01 1.300e+00
## thirtile_PGSLow PGS 1.217e+00 8.215e-01 1.067e+00 1.389e+00
## age 9.977e+00 1.002e-01 4.692e+00 2.122e+01
## sqrt(age) 1.023e-17 9.772e+16 3.616e-23 2.896e-12
## PTGENDERFemale 2.527e+00 3.957e-01 4.721e-01 1.353e+01
## age:PTGENDERFemale 9.861e-01 1.014e+00 9.630e-01 1.010e+00
##
## Concordance= 0.589 (se = 0.012 )
## Likelihood ratio test= 111.5 on 6 df, p=<2e-16
## Wald test = 127.6 on 6 df, p=<2e-16
## Score (logrank) test = 132 on 6 df, p=<2e-16## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 2228, number of events= 1376
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 4.661e-01 1.594e+00 6.908e-02 6.747 1.50e-11 ***
## thirtile_yearsHigh EA 4.678e-02 1.048e+00 6.795e-02 0.688 0.4912
## age 2.135e+00 8.457e+00 3.831e-01 5.573 2.51e-08 ***
## sqrt(age) -3.633e+01 1.674e-16 6.374e+00 -5.699 1.20e-08 ***
## PTGENDERFemale 1.854e+00 6.385e+00 8.602e-01 2.155 0.0311 *
## age:PTGENDERFemale -2.840e-02 9.720e-01 1.223e-02 -2.323 0.0202 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.594e+00 6.274e-01 1.392e+00 1.825e+00
## thirtile_yearsHigh EA 1.048e+00 9.543e-01 9.172e-01 1.197e+00
## age 8.457e+00 1.182e-01 3.991e+00 1.792e+01
## sqrt(age) 1.674e-16 5.973e+15 6.288e-22 4.458e-11
## PTGENDERFemale 6.385e+00 1.566e-01 1.183e+00 3.447e+01
## age:PTGENDERFemale 9.720e-01 1.029e+00 9.490e-01 9.956e-01
##
## Concordance= 0.631 (se = 0.011 )
## Likelihood ratio test= 156.8 on 6 df, p=<2e-16
## Wald test = 176 on 6 df, p=<2e-16
## Score (logrank) test = 180.3 on 6 df, p=<2e-16
DIGITSCORE
“The DSST (Digit Symbol Substitution Test) is a paper-and-pencil cognitive test presented on a single sheet of paper that requires a subject to match symbols to numbers according to a key located on the top of the page. The subject copies the symbol into spaces below a row of numbers. The number of correct symbols within the allowed time, usually 90 to 120 seconds, constitutes the score.” (Jaeger, 2018) The lower the scores, the more severe impairment of cognitive function.
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 762, number of events= 175
## (1354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 3.365e-01 1.400e+00 2.013e-01 1.671 0.094669 .
## thirtile_PGSLow PGS 5.562e-01 1.744e+00 2.024e-01 2.748 0.006002 **
## age -5.746e+00 3.195e-03 1.489e+00 -3.860 0.000113 ***
## sqrt(age) 9.234e+01 1.264e+40 2.469e+01 3.740 0.000184 ***
## PTGENDERFemale -1.368e+01 1.142e-06 3.271e+00 -4.183 2.88e-05 ***
## age:PTGENDERFemale 1.949e-01 1.215e+00 4.577e-02 4.260 2.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.400e+00 7.143e-01 9.435e-01 2.077e+00
## thirtile_PGSLow PGS 1.744e+00 5.734e-01 1.173e+00 2.593e+00
## age 3.195e-03 3.130e+02 1.727e-04 5.911e-02
## sqrt(age) 1.264e+40 7.913e-41 1.220e+19 1.309e+61
## PTGENDERFemale 1.142e-06 8.758e+05 1.875e-09 6.954e-04
## age:PTGENDERFemale 1.215e+00 8.229e-01 1.111e+00 1.329e+00
##
## Concordance= 0.713 (se = 0.026 )
## Likelihood ratio test= 68.15 on 6 df, p=1e-12
## Wald test = 64.79 on 6 df, p=5e-12
## Score (logrank) test = 69.4 on 6 df, p=5e-13## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 762, number of events= 175
## (1354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 1.506e+00 4.511e+00 2.342e-01 6.432 1.26e-10 ***
## thirtile_yearsHigh EA 1.985e-01 1.220e+00 2.715e-01 0.731 0.464638
## age -6.401e+00 1.659e-03 1.503e+00 -4.259 2.06e-05 ***
## sqrt(age) 1.037e+02 1.103e+45 2.489e+01 4.167 3.09e-05 ***
## PTGENDERFemale -1.313e+01 1.980e-06 3.496e+00 -3.757 0.000172 ***
## age:PTGENDERFemale 1.825e-01 1.200e+00 4.890e-02 3.733 0.000190 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 4.511e+00 2.217e-01 2.850e+00 7.139e+00
## thirtile_yearsHigh EA 1.220e+00 8.200e-01 7.164e-01 2.076e+00
## age 1.659e-03 6.026e+02 8.719e-05 3.158e-02
## sqrt(age) 1.103e+45 9.069e-46 7.173e+23 1.695e+66
## PTGENDERFemale 1.980e-06 5.049e+05 2.095e-09 1.872e-03
## age:PTGENDERFemale 1.200e+00 8.332e-01 1.091e+00 1.321e+00
##
## Concordance= 0.781 (se = 0.02 )
## Likelihood ratio test= 134.6 on 6 df, p=<2e-16
## Wald test = 120.8 on 6 df, p=<2e-16
## Score (logrank) test = 140.5 on 6 df, p=<2e-16
FAQ
The Functional Activities Questionnaire is used to assess an individual’s functional abilities in daily living activities. It is a caregiver-based questionnaire that helps evaluate how well a person is able to perform various instrumental activities of daily living (IADLs) and basic activities of daily living (ADLs). (ChatGPT) Sum scores (range 0-30). The score range for each item is 0–3 (higher scores indicate greater impairment; 0 = normal or never did but could do now; 1 = has difficulty but does by self or never did but would have difficulty now; 2 = requires assistance; 3 = dependent). There is no established cut-off score for IADL impairment on the FAQ. However, one study reported that a total FAQ score (sum of all 10 item scores; range 0–30) of ≥ 6 is suggestive of functional impairment [ 20]. (Marshall et al., 2015)
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1928, number of events= 464
## (1 observation deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.025249 0.975067 0.124797 -0.202 0.839665
## thirtile_PGSLow PGS 0.426822 1.532380 0.112776 3.785 0.000154 ***
## age -0.370943 0.690083 0.722218 -0.514 0.607520
## sqrt(age) 4.953977 141.737576 11.983585 0.413 0.679316
## PTGENDERFemale -6.227173 0.001975 1.651391 -3.771 0.000163 ***
## age:PTGENDERFemale 0.083432 1.087011 0.023425 3.562 0.000369 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 9.751e-01 1.026e+00 7.635e-01 1.245e+00
## thirtile_PGSLow PGS 1.532e+00 6.526e-01 1.228e+00 1.911e+00
## age 6.901e-01 1.449e+00 1.676e-01 2.842e+00
## sqrt(age) 1.417e+02 7.055e-03 8.934e-09 2.249e+12
## PTGENDERFemale 1.975e-03 5.063e+02 7.761e-05 5.026e-02
## age:PTGENDERFemale 1.087e+00 9.200e-01 1.038e+00 1.138e+00
##
## Concordance= 0.596 (se = 0.015 )
## Likelihood ratio test= 55.17 on 6 df, p=4e-10
## Wald test = 58.7 on 6 df, p=8e-11
## Score (logrank) test = 60.21 on 6 df, p=4e-11## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1928, number of events= 464
## (1 observation deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 7.374e-01 2.090e+00 1.109e-01 6.647 3.00e-11 ***
## thirtile_yearsHigh EA -5.445e-01 5.802e-01 1.296e-01 -4.200 2.67e-05 ***
## age -7.622e-01 4.667e-01 7.113e-01 -1.072 0.28394
## sqrt(age) 1.155e+01 1.036e+05 1.180e+01 0.979 0.32754
## PTGENDERFemale -4.401e+00 1.226e-02 1.683e+00 -2.614 0.00894 **
## age:PTGENDERFemale 5.454e-02 1.056e+00 2.391e-02 2.281 0.02255 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.090e+00 4.784e-01 1.682e+00 2.598e+00
## thirtile_yearsHigh EA 5.802e-01 1.724e+00 4.500e-01 7.480e-01
## age 4.666e-01 2.143e+00 1.157e-01 1.881e+00
## sqrt(age) 1.036e+05 9.648e-06 9.444e-06 1.137e+15
## PTGENDERFemale 1.226e-02 8.154e+01 4.525e-04 3.323e-01
## age:PTGENDERFemale 1.056e+00 9.469e-01 1.008e+00 1.107e+00
##
## Concordance= 0.677 (se = 0.014 )
## Likelihood ratio test= 148.6 on 6 df, p=<2e-16
## Wald test = 150.8 on 6 df, p=<2e-16
## Score (logrank) test = 158.2 on 6 df, p=<2e-16
LDELTOTAL
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1787, number of events= 488
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -1.110e-01 8.949e-01 1.215e-01 -0.914 0.36085
## thirtile_PGSLow PGS 3.545e-01 1.425e+00 1.084e-01 3.270 0.00108 **
## age 1.126e+00 3.084e+00 6.815e-01 1.653 0.09842 .
## sqrt(age) -1.980e+01 2.525e-09 1.130e+01 -1.752 0.07983 .
## PTGENDERFemale -4.748e+00 8.665e-03 1.521e+00 -3.122 0.00180 **
## age:PTGENDERFemale 6.744e-02 1.070e+00 2.159e-02 3.124 0.00179 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.949e-01 1.117e+00 7.053e-01 1.1356
## thirtile_PGSLow PGS 1.425e+00 7.015e-01 1.153e+00 1.7630
## age 3.084e+00 3.243e-01 8.110e-01 11.7264
## sqrt(age) 2.525e-09 3.960e+08 6.057e-19 10.5288
## PTGENDERFemale 8.665e-03 1.154e+02 4.397e-04 0.1708
## age:PTGENDERFemale 1.070e+00 9.348e-01 1.025e+00 1.1160
##
## Concordance= 0.58 (se = 0.016 )
## Likelihood ratio test= 40.25 on 6 df, p=4e-07
## Wald test = 42.49 on 6 df, p=1e-07
## Score (logrank) test = 43.19 on 6 df, p=1e-07## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1787, number of events= 488
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 8.999e-01 2.459e+00 1.103e-01 8.159 3.37e-16 ***
## thirtile_yearsHigh EA -3.048e-01 7.372e-01 1.278e-01 -2.385 0.0171 *
## age 6.531e-01 1.921e+00 6.735e-01 0.970 0.3322
## sqrt(age) -1.172e+01 8.143e-06 1.116e+01 -1.050 0.2938
## PTGENDERFemale -2.486e+00 8.321e-02 1.552e+00 -1.602 0.1092
## age:PTGENDERFemale 3.270e-02 1.033e+00 2.207e-02 1.482 0.1383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.459e+00 4.066e-01 1.981e+00 3.053e+00
## thirtile_yearsHigh EA 7.372e-01 1.356e+00 5.739e-01 9.471e-01
## age 1.921e+00 5.204e-01 5.133e-01 7.192e+00
## sqrt(age) 8.143e-06 1.228e+05 2.566e-15 2.584e+04
## PTGENDERFemale 8.321e-02 1.202e+01 3.970e-03 1.744e+00
## age:PTGENDERFemale 1.033e+00 9.678e-01 9.895e-01 1.079e+00
##
## Concordance= 0.674 (se = 0.015 )
## Likelihood ratio test= 143.2 on 6 df, p=<2e-16
## Wald test = 150.3 on 6 df, p=<2e-16
## Score (logrank) test = 162 on 6 df, p=<2e-16
MOCA
Reference literature: doi: 10.1111/j.1532-5415.2005.53221.x
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 650, number of events= 375
## (650 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.044e-01 1.110e+00 1.467e-01 0.712 0.4767
## thirtile_PGSLow PGS 4.929e-01 1.637e+00 1.223e-01 4.031 5.55e-05 ***
## age 1.288e+00 3.627e+00 7.654e-01 1.683 0.0923 .
## sqrt(age) -2.122e+01 6.072e-10 1.274e+01 -1.665 0.0959 .
## PTGENDERFemale -1.569e+00 2.083e-01 1.691e+00 -0.928 0.3536
## age:PTGENDERFemale 1.865e-02 1.019e+00 2.402e-02 0.777 0.4374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.110e+00 9.009e-01 8.326e-01 1.480
## thirtile_PGSLow PGS 1.637e+00 6.108e-01 1.288e+00 2.080
## age 3.627e+00 2.757e-01 8.092e-01 16.259
## sqrt(age) 6.072e-10 1.647e+09 8.612e-21 42.809
## PTGENDERFemale 2.083e-01 4.800e+00 7.572e-03 5.732
## age:PTGENDERFemale 1.019e+00 9.815e-01 9.720e-01 1.068
##
## Concordance= 0.622 (se = 0.022 )
## Likelihood ratio test= 33.7 on 6 df, p=8e-06
## Wald test = 33.88 on 6 df, p=7e-06
## Score (logrank) test = 34.44 on 6 df, p=6e-06## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 650, number of events= 375
## (650 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 6.722e-01 1.958e+00 1.259e-01 5.339 9.37e-08 ***
## thirtile_yearsHigh EA -1.533e-01 8.579e-01 1.362e-01 -1.125 0.261
## age 1.171e+00 3.226e+00 7.621e-01 1.537 0.124
## sqrt(age) -1.940e+01 3.748e-09 1.268e+01 -1.530 0.126
## PTGENDERFemale -1.140e+00 3.197e-01 1.703e+00 -0.670 0.503
## age:PTGENDERFemale 1.011e-02 1.010e+00 2.421e-02 0.418 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.958e+00 5.106e-01 1.530e+00 2.507
## thirtile_yearsHigh EA 8.579e-01 1.166e+00 6.569e-01 1.120
## age 3.226e+00 3.100e-01 7.243e-01 14.364
## sqrt(age) 3.748e-09 2.668e+08 6.021e-20 233.279
## PTGENDERFemale 3.197e-01 3.127e+00 1.136e-02 9.002
## age:PTGENDERFemale 1.010e+00 9.899e-01 9.634e-01 1.059
##
## Concordance= 0.671 (se = 0.021 )
## Likelihood ratio test= 60.33 on 6 df, p=4e-11
## Wald test = 61.25 on 6 df, p=3e-11
## Score (logrank) test = 63.03 on 6 df, p=1e-11
Rey-Auditory Verbal Learning Test (RAVLT)
The RAVLT was included as a measure of memory function. In this test, the participants are asked to recall words from a list of 15 nouns immediately after each of five learning trials and after a short and a long delay. Two measures known to be sensitive to cognitive changes in patients with AD were included in the present study: Immediate recall (RAVLT-Im): the number of correct responses across the immediate recall of the five learning trials; percent forgetting (RAVLT-PF): the score on the fifth learning trial minus the score on the long delayed recall, divided by the score obtained on the fifth learning trial. The lower the scores, the more severe impairment of cognitive function.
Different summary scores are derived from raw RAVLT scores. These include RAVLT Immediate (the sum of scores from 5 first trials (Trials 1 to 5)), RAVLT Learning (the score of Trial 5 minus the score of Trial 1), RAVLT Forgetting (the score of Trial 5 minus score of the delayed recall) and RAVLT Percent Forgetting (RAVLT Forgetting divided by the score of Trial 5). We use naming of the ADNI merge table3 for these summary measures. We investigated the relationship between MRI measures and RAVLT cognitive test scores by estimating the RAVLT Immediate and RAVLT Percent Forgetting from the gray matter density. These two summary scores were selected since they highlight different aspects of episodic memory, learning (RAVLT Immediate) and delayed memory (RAVLT Percent forgetting), essential to AD and previous studies (Estévez-González et al., 2003, Wang et al., 2011, Gomar et al., 2014, Moradi et al., 2015) have indicated strong relationships between these two RAVLT measures and Alzheimer’s disease.
RAVLT Immediate
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1188, number of events= 279
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 0.05694 1.05859 0.16491 0.345 0.7299
## thirtile_PGSLow PGS 0.33342 1.39573 0.14483 2.302 0.0213 *
## age -0.58116 0.55925 0.94938 -0.612 0.5404
## sqrt(age) 8.25968 3864.86093 15.73108 0.525 0.5995
## PTGENDERFemale -4.32807 0.01319 2.04351 -2.118 0.0342 *
## age:PTGENDERFemale 0.05977 1.06159 0.02910 2.054 0.0400 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.059e+00 9.447e-01 7.662e-01 1.463e+00
## thirtile_PGSLow PGS 1.396e+00 7.165e-01 1.051e+00 1.854e+00
## age 5.593e-01 1.788e+00 8.699e-02 3.595e+00
## sqrt(age) 3.865e+03 2.587e-04 1.573e-10 9.494e+16
## PTGENDERFemale 1.319e-02 7.580e+01 2.404e-04 7.241e-01
## age:PTGENDERFemale 1.062e+00 9.420e-01 1.003e+00 1.124e+00
##
## Concordance= 0.584 (se = 0.02 )
## Likelihood ratio test= 26.17 on 6 df, p=2e-04
## Wald test = 27.89 on 6 df, p=1e-04
## Score (logrank) test = 28.52 on 6 df, p=7e-05## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 1188, number of events= 279
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 0.44529 1.56094 0.13397 3.324 0.000888 ***
## thirtile_yearsHigh EA -1.40580 0.24517 0.20267 -6.936 4.02e-12 ***
## age -0.19371 0.82389 0.94810 -0.204 0.838104
## sqrt(age) 1.77195 5.88231 15.71794 0.113 0.910241
## PTGENDERFemale -1.23767 0.29006 2.06147 -0.600 0.548252
## age:PTGENDERFemale 0.01442 1.01453 0.02941 0.490 0.623838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.5609 0.6406 1.200e+00 2.030e+00
## thirtile_yearsHigh EA 0.2452 4.0788 1.648e-01 3.647e-01
## age 0.8239 1.2138 1.285e-01 5.283e+00
## sqrt(age) 5.8823 0.1700 2.457e-13 1.408e+14
## PTGENDERFemale 0.2901 3.4476 5.102e-03 1.649e+01
## age:PTGENDERFemale 1.0145 0.9857 9.577e-01 1.075e+00
##
## Concordance= 0.72 (se = 0.017 )
## Likelihood ratio test= 133.5 on 6 df, p=<2e-16
## Wald test = 109 on 6 df, p=<2e-16
## Score (logrank) test = 124.2 on 6 df, p=<2e-16
RAVLT Percentage Forgetting
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 979, number of events= 260
## (15 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -0.25401 0.77569 0.17213 -1.476 0.1400
## thirtile_PGSLow PGS 0.23361 1.26315 0.14485 1.613 0.1068
## age -0.39021 0.67692 0.95766 -0.407 0.6837
## sqrt(age) 5.16646 175.29383 15.86365 0.326 0.7447
## PTGENDERFemale -3.37219 0.03431 2.02250 -1.667 0.0954 .
## age:PTGENDERFemale 0.04714 1.04826 0.02889 1.632 0.1028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 0.77569 1.289182 5.536e-01 1.087e+00
## thirtile_PGSLow PGS 1.26315 0.791669 9.509e-01 1.678e+00
## age 0.67692 1.477285 1.036e-01 4.423e+00
## sqrt(age) 175.29383 0.005705 5.503e-12 5.584e+15
## PTGENDERFemale 0.03431 29.142264 6.515e-04 1.807e+00
## age:PTGENDERFemale 1.04826 0.953958 9.906e-01 1.109e+00
##
## Concordance= 0.587 (se = 0.021 )
## Likelihood ratio test= 27.49 on 6 df, p=1e-04
## Wald test = 28.5 on 6 df, p=8e-05
## Score (logrank) test = 29.15 on 6 df, p=6e-05## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 979, number of events= 260
## (15 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 7.708e-01 2.162e+00 1.449e-01 5.320 1.04e-07 ***
## thirtile_yearsHigh EA -9.076e-01 4.035e-01 1.920e-01 -4.727 2.28e-06 ***
## age -7.747e-01 4.608e-01 9.722e-01 -0.797 0.426
## sqrt(age) 1.145e+01 9.395e+04 1.611e+01 0.711 0.477
## PTGENDERFemale -9.756e-01 3.770e-01 2.046e+00 -0.477 0.634
## age:PTGENDERFemale 1.001e-02 1.010e+00 2.931e-02 0.342 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.162e+00 4.626e-01 1.627e+00 2.871e+00
## thirtile_yearsHigh EA 4.035e-01 2.478e+00 2.769e-01 5.878e-01
## age 4.608e-01 2.170e+00 6.855e-02 3.098e+00
## sqrt(age) 9.395e+04 1.064e-05 1.820e-09 4.851e+18
## PTGENDERFemale 3.770e-01 2.653e+00 6.830e-03 2.081e+01
## age:PTGENDERFemale 1.010e+00 9.900e-01 9.537e-01 1.070e+00
##
## Concordance= 0.724 (se = 0.019 )
## Likelihood ratio test= 114.5 on 6 df, p=<2e-16
## Wald test = 108.5 on 6 df, p=<2e-16
## Score (logrank) test = 118 on 6 df, p=<2e-16
RAVLT Forgetting
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 964, number of events= 6
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -2.106e-01 8.101e-01 1.044e+00 -0.202 0.840
## thirtile_PGSLow PGS -2.447e-01 7.830e-01 1.020e+00 -0.240 0.810
## age -1.317e+00 2.681e-01 6.070e+00 -0.217 0.828
## sqrt(age) 1.280e+01 3.624e+05 9.746e+01 0.131 0.896
## PTGENDERFemale -2.675e+01 2.425e-12 2.899e+01 -0.923 0.356
## age:PTGENDERFemale 4.351e-01 1.545e+00 4.549e-01 0.957 0.339
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.101e-01 1.234e+00 1.047e-01 6.268e+00
## thirtile_PGSLow PGS 7.830e-01 1.277e+00 1.060e-01 5.783e+00
## age 2.681e-01 3.731e+00 1.825e-06 3.937e+04
## sqrt(age) 3.624e+05 2.759e-06 4.007e-78 3.278e+88
## PTGENDERFemale 2.425e-12 4.124e+11 5.095e-37 1.154e+13
## age:PTGENDERFemale 1.545e+00 6.472e-01 6.335e-01 3.768e+00
##
## Concordance= 0.765 (se = 0.071 )
## Likelihood ratio test= 8.98 on 6 df, p=0.2
## Wald test = 2.83 on 6 df, p=0.8
## Score (logrank) test = 7.69 on 6 df, p=0.3## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 964, number of events= 6
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -2.115e-01 8.094e-01 1.434e+00 -0.148 0.883
## thirtile_yearsHigh EA 9.571e-01 2.604e+00 1.146e+00 0.835 0.404
## age -1.601e+00 2.018e-01 5.953e+00 -0.269 0.788
## sqrt(age) 1.767e+01 4.707e+07 9.574e+01 0.185 0.854
## PTGENDERFemale -2.896e+01 2.649e-13 2.838e+01 -1.020 0.308
## age:PTGENDERFemale 4.663e-01 1.594e+00 4.457e-01 1.046 0.295
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 8.094e-01 1.236e+00 4.874e-02 1.344e+01
## thirtile_yearsHigh EA 2.604e+00 3.840e-01 2.753e-01 2.463e+01
## age 2.018e-01 4.956e+00 1.727e-06 2.357e+04
## sqrt(age) 4.707e+07 2.124e-08 1.498e-74 1.479e+89
## PTGENDERFemale 2.649e-13 3.776e+12 1.837e-37 3.819e+11
## age:PTGENDERFemale 1.594e+00 6.273e-01 6.654e-01 3.819e+00
##
## Concordance= 0.776 (se = 0.09 )
## Likelihood ratio test= 10.38 on 6 df, p=0.1
## Wald test = 4.12 on 6 df, p=0.7
## Score (logrank) test = 9.35 on 6 df, p=0.2
RAVLT Learning
################################################################################
# Run Cox Proportional Hazards Model & Plot
################################################################################
# Creates Cox Proportional Hazards regression model
res.cox.PGS <- fit_coxph_PGS("RAVLT_learning_cut")
res.cox.years <- fit_coxph_years("RAVLT_learning_cut")
# Results of Models
summary(res.cox.PGS)
summary(res.cox.years)
# Plots the Cox model
plot_coxph_PGS(res.cox.PGS)
plot_coxph_years(res.cox.years)TRABSCORE
The Trail Making Test is a neuropsychological test of visual attention and task switching. It has two parts, in which the subject is instructed to connect a set of 25 dots as quickly as possible while maintaining accuracy.
The test can provide information about visual search speed, scanning, speed of processing, mental flexibility, and executive functioning. It is sensitive to cognitive impairment associated with dementia, including Alzheimer’s disease. (ChatGPT)
Record the total number of seconds to complete Part B (Trails B), up to a maximum of 300 seconds. If the participant is not finished by 300 seconds, the score is 300.
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 786, number of events= 165
## (34 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -2.391e-01 7.873e-01 2.184e-01 -1.095 0.274
## thirtile_PGSLow PGS 1.264e-01 1.135e+00 1.844e-01 0.685 0.493
## age -6.441e-01 5.251e-01 1.319e+00 -0.488 0.625
## sqrt(age) 1.053e+01 3.753e+04 2.191e+01 0.481 0.631
## PTGENDERFemale 9.816e-01 2.669e+00 2.731e+00 0.359 0.719
## age:PTGENDERFemale -9.235e-03 9.908e-01 3.863e-02 -0.239 0.811
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 7.873e-01 1.270e+00 5.132e-01 1.208e+00
## thirtile_PGSLow PGS 1.135e+00 8.813e-01 7.905e-01 1.629e+00
## age 5.251e-01 1.904e+00 3.961e-02 6.962e+00
## sqrt(age) 3.753e+04 2.665e-05 8.346e-15 1.688e+23
## PTGENDERFemale 2.669e+00 3.747e-01 1.263e-02 5.640e+02
## age:PTGENDERFemale 9.908e-01 1.009e+00 9.186e-01 1.069e+00
##
## Concordance= 0.575 (se = 0.026 )
## Likelihood ratio test= 8.31 on 6 df, p=0.2
## Wald test = 8.24 on 6 df, p=0.2
## Score (logrank) test = 8.3 on 6 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 786, number of events= 165
## (34 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 1.408e+00 4.086e+00 2.075e-01 6.784 1.17e-11 ***
## thirtile_yearsHigh EA -1.235e-01 8.838e-01 2.398e-01 -0.515 0.6065
## age -2.514e+00 8.097e-02 1.378e+00 -1.824 0.0681 .
## sqrt(age) 4.163e+01 1.202e+18 2.291e+01 1.817 0.0692 .
## PTGENDERFemale 3.575e+00 3.570e+01 2.740e+00 1.305 0.1919
## age:PTGENDERFemale -5.172e-02 9.496e-01 3.890e-02 -1.330 0.1837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 4.086e+00 2.447e-01 2.720859 6.137e+00
## thirtile_yearsHigh EA 8.838e-01 1.131e+00 0.552360 1.414e+00
## age 8.097e-02 1.235e+01 0.005438 1.206e+00
## sqrt(age) 1.202e+18 8.323e-19 0.037997 3.799e+37
## PTGENDERFemale 3.570e+01 2.801e-02 0.166214 7.668e+03
## age:PTGENDERFemale 9.496e-01 1.053e+00 0.879891 1.025e+00
##
## Concordance= 0.692 (se = 0.025 )
## Likelihood ratio test= 79.51 on 6 df, p=5e-15
## Wald test = 79.64 on 6 df, p=4e-15
## Score (logrank) test = 91 on 6 df, p=<2e-16
Patient’s Everyday Cognition (EcogPt)
The original version of the ECog is an informant-based measure of cognitively-relevant everyday abilities comprised of 39 items, covering six cognitively-relevant domains: Everyday Memory, Everyday Language, Everyday Visuospatial Abilities, and Everyday Planning, Everyday Organization, and Everyday Divided Attention. Ratings are made on a four-point scale: 1 = better or no change compared to 10 years earlier, 2 = questionable/occasionally worse, 3 = consistently a little worse, 4 = consistently much worse. (Tomaszewski Farias et al., 2012)
EcogPt Everyday Divided Attention
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 398, number of events= 10
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -6.726e-02 9.350e-01 7.155e-01 -0.094 0.925
## thirtile_PGSLow PGS -1.430e+00 2.394e-01 1.124e+00 -1.272 0.203
## age 1.031e+00 2.804e+00 4.682e+00 0.220 0.826
## sqrt(age) -2.046e+01 1.304e-09 7.710e+01 -0.265 0.791
## PTGENDERFemale -7.446e+00 5.840e-04 1.123e+01 -0.663 0.507
## age:PTGENDERFemale 1.173e-01 1.125e+00 1.660e-01 0.707 0.480
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 9.350e-01 1.070e+00 2.300e-01 3.800e+00
## thirtile_PGSLow PGS 2.394e-01 4.177e+00 2.647e-02 2.166e+00
## age 2.804e+00 3.567e-01 2.901e-04 2.710e+04
## sqrt(age) 1.304e-09 7.668e+08 3.100e-75 5.486e+56
## PTGENDERFemale 5.840e-04 1.712e+03 1.611e-13 2.118e+06
## age:PTGENDERFemale 1.125e+00 8.893e-01 8.122e-01 1.557e+00
##
## Concordance= 0.764 (se = 0.064 )
## Likelihood ratio test= 6.96 on 6 df, p=0.3
## Wald test = 5.16 on 6 df, p=0.5
## Score (logrank) test = 6.3 on 6 df, p=0.4## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 398, number of events= 10
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -4.810e-02 9.530e-01 9.438e-01 -0.051 0.959
## thirtile_yearsHigh EA 5.419e-01 1.719e+00 7.484e-01 0.724 0.469
## age 1.746e+00 5.731e+00 4.726e+00 0.369 0.712
## sqrt(age) -3.264e+01 6.674e-15 7.785e+01 -0.419 0.675
## PTGENDERFemale -8.232e+00 2.660e-04 1.139e+01 -0.723 0.470
## age:PTGENDERFemale 1.304e-01 1.139e+00 1.683e-01 0.775 0.438
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 9.530e-01 1.049e+00 1.499e-01 6.060e+00
## thirtile_yearsHigh EA 1.719e+00 5.816e-01 3.966e-01 7.453e+00
## age 5.731e+00 1.745e-01 5.439e-04 6.039e+04
## sqrt(age) 6.674e-15 1.498e+14 3.649e-81 1.220e+52
## PTGENDERFemale 2.660e-04 3.760e+03 5.405e-14 1.309e+06
## age:PTGENDERFemale 1.139e+00 8.778e-01 8.192e-01 1.584e+00
##
## Concordance= 0.746 (se = 0.083 )
## Likelihood ratio test= 5.26 on 6 df, p=0.5
## Wald test = 4.35 on 6 df, p=0.6
## Score (logrank) test = 5.06 on 6 df, p=0.5
EcogPt Everyday Language
## Warning in agreg.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 2 ; beta may be infinite.## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 372, number of events= 13
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.486e+00 4.419e+00 8.069e-01 1.841 0.0656 .
## thirtile_PGSLow PGS -1.902e+01 5.474e-09 9.415e+03 -0.002 0.9984
## age 7.068e+00 1.173e+03 3.954e+00 1.787 0.0739 .
## sqrt(age) -1.187e+02 2.693e-52 6.515e+01 -1.822 0.0684 .
## PTGENDERFemale -3.003e+00 4.961e-02 8.691e+00 -0.346 0.7296
## age:PTGENDERFemale 5.282e-02 1.054e+00 1.285e-01 0.411 0.6811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 4.419e+00 2.263e-01 9.088e-01 2.149e+01
## thirtile_PGSLow PGS 5.474e-09 1.827e+08 0.000e+00 Inf
## age 1.173e+03 8.522e-04 5.054e-01 2.725e+06
## sqrt(age) 2.693e-52 3.713e+51 9.338e-108 7.768e+03
## PTGENDERFemale 4.961e-02 2.016e+01 1.986e-09 1.239e+06
## age:PTGENDERFemale 1.054e+00 9.486e-01 8.195e-01 1.356e+00
##
## Concordance= 0.892 (se = 0.03 )
## Likelihood ratio test= 22.89 on 6 df, p=8e-04
## Wald test = 9.7 on 6 df, p=0.1
## Score (logrank) test = 23.24 on 6 df, p=7e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 372, number of events= 13
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -5.234e-01 5.925e-01 7.546e-01 -0.694 0.4880
## thirtile_yearsHigh EA -4.443e-01 6.413e-01 6.617e-01 -0.671 0.5020
## age 7.158e+00 1.285e+03 3.811e+00 1.879 0.0603 .
## sqrt(age) -1.218e+02 1.211e-53 6.299e+01 -1.934 0.0531 .
## PTGENDERFemale -4.424e+00 1.199e-02 8.909e+00 -0.497 0.6195
## age:PTGENDERFemale 8.355e-02 1.087e+00 1.316e-01 0.635 0.5254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 5.925e-01 1.688e+00 1.350e-01 2.600e+00
## thirtile_yearsHigh EA 6.413e-01 1.559e+00 1.753e-01 2.346e+00
## age 1.285e+03 7.782e-04 7.334e-01 2.251e+06
## sqrt(age) 1.211e-53 8.260e+52 2.942e-107 4.981e+00
## PTGENDERFemale 1.199e-02 8.344e+01 3.127e-10 4.594e+05
## age:PTGENDERFemale 1.087e+00 9.198e-01 8.400e-01 1.407e+00
##
## Concordance= 0.748 (se = 0.081 )
## Likelihood ratio test= 10.62 on 6 df, p=0.1
## Wald test = 9.28 on 6 df, p=0.2
## Score (logrank) test = 11.5 on 6 df, p=0.07
EcogPt Everyday Memory
## Warning in agreg.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 2 ; beta may be infinite.## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 343, number of events= 14
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 5.669e-01 1.763e+00 5.924e-01 0.957 0.339
## thirtile_PGSLow PGS -1.946e+01 3.547e-09 7.819e+03 -0.002 0.998
## age -2.219e+00 1.087e-01 4.832e+00 -0.459 0.646
## sqrt(age) 3.629e+01 5.753e+15 7.992e+01 0.454 0.650
## PTGENDERFemale 1.615e+01 1.035e+07 1.126e+01 1.434 0.151
## age:PTGENDERFemale -2.420e-01 7.851e-01 1.672e-01 -1.448 0.148
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.763e+00 5.673e-01 5.519e-01 5.629e+00
## thirtile_PGSLow PGS 3.547e-09 2.820e+08 0.000e+00 Inf
## age 1.087e-01 9.197e+00 8.383e-06 1.410e+03
## sqrt(age) 5.753e+15 1.738e-16 5.421e-53 6.106e+83
## PTGENDERFemale 1.035e+07 9.658e-08 2.693e-03 3.980e+16
## age:PTGENDERFemale 7.851e-01 1.274e+00 5.658e-01 1.089e+00
##
## Concordance= 0.848 (se = 0.045 )
## Likelihood ratio test= 18.67 on 6 df, p=0.005
## Wald test = 6.02 on 6 df, p=0.4
## Score (logrank) test = 15.17 on 6 df, p=0.02## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 343, number of events= 14
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -7.985e-02 9.233e-01 7.362e-01 -0.108 0.9136
## thirtile_yearsHigh EA -1.431e+00 2.391e-01 8.034e-01 -1.781 0.0749 .
## age -3.745e+00 2.364e-02 5.233e+00 -0.716 0.4743
## sqrt(age) 6.140e+01 4.653e+26 8.684e+01 0.707 0.4795
## PTGENDERFemale 1.863e+01 1.229e+08 1.202e+01 1.549 0.1213
## age:PTGENDERFemale -2.716e-01 7.622e-01 1.781e-01 -1.525 0.1272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 9.233e-01 1.083e+00 2.181e-01 3.908e+00
## thirtile_yearsHigh EA 2.390e-01 4.183e+00 4.951e-02 1.154e+00
## age 2.364e-02 4.229e+01 8.305e-07 6.732e+02
## sqrt(age) 4.653e+26 2.149e-27 5.636e-48 3.841e+100
## PTGENDERFemale 1.229e+08 8.134e-09 7.178e-03 2.106e+18
## age:PTGENDERFemale 7.622e-01 1.312e+00 5.376e-01 1.080e+00
##
## Concordance= 0.748 (se = 0.058 )
## Likelihood ratio test= 12.13 on 6 df, p=0.06
## Wald test = 9.98 on 6 df, p=0.1
## Score (logrank) test = 11.83 on 6 df, p=0.07
EcogPt Everyday Organization
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 316, number of events= 12
## (394 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 6.070e-01 1.835e+00 8.360e-01 0.726 0.468
## thirtile_PGSLow PGS 4.402e-01 1.553e+00 8.735e-01 0.504 0.614
## age 4.478e+00 8.808e+01 3.526e+00 1.270 0.204
## sqrt(age) -7.725e+01 2.815e-34 5.851e+01 -1.320 0.187
## PTGENDERFemale -3.608e+00 2.712e-02 9.192e+00 -0.392 0.695
## age:PTGENDERFemale 5.940e-02 1.061e+00 1.340e-01 0.443 0.658
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.835e+00 5.450e-01 3.564e-01 9.446e+00
## thirtile_PGSLow PGS 1.553e+00 6.439e-01 2.803e-01 8.604e+00
## age 8.808e+01 1.135e-02 8.786e-02 8.830e+04
## sqrt(age) 2.815e-34 3.553e+33 4.464e-84 1.775e+16
## PTGENDERFemale 2.712e-02 3.688e+01 4.063e-10 1.810e+06
## age:PTGENDERFemale 1.061e+00 9.423e-01 8.161e-01 1.380e+00
##
## Concordance= 0.693 (se = 0.096 )
## Likelihood ratio test= 6.88 on 6 df, p=0.3
## Wald test = 7.41 on 6 df, p=0.3
## Score (logrank) test = 8.91 on 6 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 316, number of events= 12
## (394 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 6.852e-01 1.984e+00 7.458e-01 0.919 0.358
## thirtile_yearsHigh EA -3.856e-01 6.800e-01 7.943e-01 -0.486 0.627
## age 2.819e+00 1.675e+01 3.777e+00 0.746 0.456
## sqrt(age) -4.953e+01 3.088e-22 6.283e+01 -0.788 0.431
## PTGENDERFemale -2.866e+00 5.693e-02 9.660e+00 -0.297 0.767
## age:PTGENDERFemale 4.906e-02 1.050e+00 1.402e-01 0.350 0.726
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.984e+00 5.040e-01 4.600e-01 8.559e+00
## thirtile_yearsHigh EA 6.800e-01 1.471e+00 1.434e-01 3.226e+00
## age 1.675e+01 5.969e-02 1.021e-02 2.750e+04
## sqrt(age) 3.088e-22 3.238e+21 1.016e-75 9.382e+31
## PTGENDERFemale 5.693e-02 1.756e+01 3.410e-10 9.506e+06
## age:PTGENDERFemale 1.050e+00 9.521e-01 7.979e-01 1.383e+00
##
## Concordance= 0.778 (se = 0.073 )
## Likelihood ratio test= 8.21 on 6 df, p=0.2
## Wald test = 9.18 on 6 df, p=0.2
## Score (logrank) test = 11.18 on 6 df, p=0.08
EcogPt Everyday Planning
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 298, number of events= 16
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.859e+00 6.416e+00 8.206e-01 2.265 0.0235 *
## thirtile_PGSLow PGS 5.246e-01 1.690e+00 8.723e-01 0.601 0.5476
## age 9.060e+00 8.602e+03 4.521e+00 2.004 0.0451 *
## sqrt(age) -1.545e+02 8.273e-68 7.662e+01 -2.016 0.0438 *
## PTGENDERFemale 6.562e-01 1.927e+00 1.065e+01 0.062 0.9509
## age:PTGENDERFemale -2.111e-02 9.791e-01 1.505e-01 -0.140 0.8885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 6.416e+00 1.559e-01 1.285e+00 3.204e+01
## thirtile_PGSLow PGS 1.690e+00 5.918e-01 3.057e-01 9.341e+00
## age 8.602e+03 1.163e-04 1.220e+00 6.065e+07
## sqrt(age) 8.273e-68 1.209e+67 4.978e-133 1.375e-02
## PTGENDERFemale 1.927e+00 5.188e-01 1.654e-09 2.246e+09
## age:PTGENDERFemale 9.791e-01 1.021e+00 7.290e-01 1.315e+00
##
## Concordance= 0.783 (se = 0.078 )
## Likelihood ratio test= 18.91 on 6 df, p=0.004
## Wald test = 18.03 on 6 df, p=0.006
## Score (logrank) test = 24.04 on 6 df, p=5e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 298, number of events= 16
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 7.994e-01 2.224e+00 8.933e-01 0.895 0.371
## thirtile_yearsHigh EA 8.280e-01 2.289e+00 6.317e-01 1.311 0.190
## age 6.582e+00 7.220e+02 4.176e+00 1.576 0.115
## sqrt(age) -1.125e+02 1.365e-49 7.055e+01 -1.595 0.111
## PTGENDERFemale 4.710e+00 1.111e+02 1.038e+01 0.454 0.650
## age:PTGENDERFemale -7.356e-02 9.291e-01 1.472e-01 -0.500 0.617
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.224e+00 4.496e-01 3.862e-01 1.281e+01
## thirtile_yearsHigh EA 2.289e+00 4.369e-01 6.637e-01 7.894e+00
## age 7.220e+02 1.385e-03 2.015e-01 2.587e+06
## sqrt(age) 1.365e-49 7.326e+48 1.213e-109 1.536e+11
## PTGENDERFemale 1.111e+02 9.003e-03 1.623e-07 7.600e+10
## age:PTGENDERFemale 9.291e-01 1.076e+00 6.962e-01 1.240e+00
##
## Concordance= 0.73 (se = 0.073 )
## Likelihood ratio test= 13.14 on 6 df, p=0.04
## Wald test = 14.06 on 6 df, p=0.03
## Score (logrank) test = 18.23 on 6 df, p=0.006
EcogPt Everyday Visuospatial Abilities
## Warning in agreg.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 1,2 ; beta may be infinite.## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 281, number of events= 8
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 2.638e+00 1.398e+01 1.411e+00 1.870 0.0615 .
## thirtile_PGSLow PGS 1.085e-02 1.011e+00 1.590e+00 0.007 0.9946
## age -1.354e+02 1.564e-59 9.976e+01 -1.357 0.1747
## sqrt(age) 2.291e+03 Inf 1.689e+03 1.356 0.1750
## PTGENDERFemale 7.838e+02 Inf 4.944e+02 1.585 0.1129
## age:PTGENDERFemale -1.182e+01 7.345e-06 7.395e+00 -1.599 0.1099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.398e+01 7.151e-02 8.810e-01 2.220e+02
## thirtile_PGSLow PGS 1.011e+00 9.892e-01 4.480e-02 2.281e+01
## age 1.564e-59 6.396e+58 1.899e-144 1.287e+26
## sqrt(age) Inf 0.000e+00 0.000e+00 Inf
## PTGENDERFemale Inf 0.000e+00 3.722e-81 Inf
## age:PTGENDERFemale 7.345e-06 1.361e+05 3.726e-12 1.448e+01
##
## Concordance= 0.981 (se = 0.014 )
## Likelihood ratio test= 39.37 on 6 df, p=6e-07
## Wald test = 9.59 on 6 df, p=0.1
## Score (logrank) test = 23.94 on 6 df, p=5e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 281, number of events= 8
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 1.099e+01 5.906e+04 1.845e+04 0.001 0.9995
## thirtile_yearsHigh EA 2.117e+01 1.565e+09 1.845e+04 0.001 0.9991
## age -1.271e+02 6.453e-56 8.725e+01 -1.457 0.1452
## sqrt(age) 2.146e+03 Inf 1.473e+03 1.456 0.1453
## PTGENDERFemale 9.444e+02 Inf 5.135e+02 1.839 0.0659 .
## age:PTGENDERFemale -1.438e+01 5.710e-07 7.800e+00 -1.843 0.0653 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 5.906e+04 1.693e-05 0.000e+00 Inf
## thirtile_yearsHigh EA 1.565e+09 6.390e-10 0.000e+00 Inf
## age 6.453e-56 1.550e+55 3.503e-130 1.189e+19
## sqrt(age) Inf 0.000e+00 4.941e-323 Inf
## PTGENDERFemale Inf 0.000e+00 1.101e-27 Inf
## age:PTGENDERFemale 5.710e-07 1.751e+06 1.310e-13 2.489e+00
##
## Concordance= 0.978 (se = 0.014 )
## Likelihood ratio test= 42.99 on 6 df, p=1e-07
## Wald test = 3.47 on 6 df, p=0.7
## Score (logrank) test = 22.49 on 6 df, p=0.001
EcogPt Total
## Warning in agreg.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
## Warning in agreg.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 281, number of events= 4
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 2.292e+01 8.991e+09 3.327e+04 0.001 0.999
## thirtile_PGSLow PGS -5.106e-02 9.502e-01 4.197e+04 0.000 1.000
## age 1.541e+01 4.944e+06 1.445e+01 1.067 0.286
## sqrt(age) -2.582e+02 7.388e-113 2.445e+02 -1.056 0.291
## PTGENDERFemale 1.870e+01 1.329e+08 4.126e+01 0.453 0.650
## age:PTGENDERFemale -2.827e-01 7.537e-01 5.726e-01 -0.494 0.621
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.991e+09 1.112e-10 0.000e+00 Inf
## thirtile_PGSLow PGS 9.502e-01 1.052e+00 0.000e+00 Inf
## age 4.944e+06 2.023e-07 2.492e-06 9.807e+18
## sqrt(age) 7.388e-113 1.354e+112 5.168e-321 1.056e+96
## PTGENDERFemale 1.329e+08 7.525e-09 1.009e-27 1.749e+43
## age:PTGENDERFemale 7.537e-01 1.327e+00 2.454e-01 2.315e+00
##
## Concordance= 0.953 (se = 0.036 )
## Likelihood ratio test= 14.9 on 6 df, p=0.02
## Wald test = 3.78 on 6 df, p=0.7
## Score (logrank) test = 16.26 on 6 df, p=0.01## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 281, number of events= 4
## (382 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA -2.040e+01 1.383e-09 1.392e+04 -0.001 0.999
## thirtile_yearsHigh EA 3.933e-01 1.482e+00 1.328e+00 0.296 0.767
## age 1.121e+01 7.417e+04 1.161e+01 0.966 0.334
## sqrt(age) -1.896e+02 4.681e-83 1.970e+02 -0.962 0.336
## PTGENDERFemale 2.068e+01 9.575e+08 2.919e+01 0.708 0.479
## age:PTGENDERFemale -2.919e-01 7.468e-01 4.164e-01 -0.701 0.483
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.383e-09 7.233e+08 0.000e+00 Inf
## thirtile_yearsHigh EA 1.482e+00 6.748e-01 1.098e-01 1.999e+01
## age 7.417e+04 1.348e-05 9.788e-06 5.620e+14
## sqrt(age) 4.681e-83 2.136e+82 9.293e-251 2.358e+85
## PTGENDERFemale 9.575e+08 1.044e-09 1.352e-16 6.781e+33
## age:PTGENDERFemale 7.468e-01 1.339e+00 3.302e-01 1.689e+00
##
## Concordance= 0.782 (se = 0.154 )
## Likelihood ratio test= 8.82 on 6 df, p=0.2
## Wald test = 4.83 on 6 df, p=0.6
## Score (logrank) test = 9.52 on 6 df, p=0.1
Self-Reported Everyday Cognitive Abilities Questionnaire (EcogSP)
EcogSP Everyday Divided Attention
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 251, number of events= 26
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.604e-02 1.016e+00 4.501e-01 0.036 0.9716
## thirtile_PGSLow PGS -1.565e+00 2.092e-01 6.472e-01 -2.418 0.0156 *
## age -1.509e+00 2.212e-01 5.248e+00 -0.287 0.7738
## sqrt(age) 2.816e+01 1.697e+12 8.876e+01 0.317 0.7511
## PTGENDERFemale 3.796e+01 3.063e+16 1.667e+01 2.277 0.0228 *
## age:PTGENDERFemale -5.614e-01 5.704e-01 2.428e-01 -2.312 0.0208 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.016e+00 9.841e-01 4.205e-01 2.455e+00
## thirtile_PGSLow PGS 2.092e-01 4.781e+00 5.882e-02 7.437e-01
## age 2.212e-01 4.521e+00 7.539e-06 6.489e+03
## sqrt(age) 1.697e+12 5.894e-13 4.715e-64 6.105e+87
## PTGENDERFemale 3.063e+16 3.265e-17 1.983e+02 4.731e+30
## age:PTGENDERFemale 5.704e-01 1.753e+00 3.544e-01 9.180e-01
##
## Concordance= 0.786 (se = 0.042 )
## Likelihood ratio test= 26.97 on 6 df, p=1e-04
## Wald test = 16.64 on 6 df, p=0.01
## Score (logrank) test = 22.87 on 6 df, p=8e-04## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 251, number of events= 26
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 4.912e-01 1.634e+00 6.647e-01 0.739 0.4599
## thirtile_yearsHigh EA -3.730e-02 9.634e-01 4.499e-01 -0.083 0.9339
## age -3.795e+00 2.249e-02 5.486e+00 -0.692 0.4891
## sqrt(age) 6.589e+01 4.145e+28 9.274e+01 0.711 0.4774
## PTGENDERFemale 3.972e+01 1.776e+17 1.764e+01 2.251 0.0244 *
## age:PTGENDERFemale -5.860e-01 5.566e-01 2.571e-01 -2.280 0.0226 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.634e+00 6.119e-01 4.442e-01 6.014e+00
## thirtile_yearsHigh EA 9.634e-01 1.038e+00 3.989e-01 2.327e+00
## age 2.249e-02 4.447e+01 4.815e-07 1.050e+03
## sqrt(age) 4.145e+28 2.413e-29 4.792e-51 3.585e+107
## PTGENDERFemale 1.776e+17 5.631e-18 1.711e+02 1.843e+32
## age:PTGENDERFemale 5.566e-01 1.797e+00 3.363e-01 9.211e-01
##
## Concordance= 0.76 (se = 0.052 )
## Likelihood ratio test= 18.82 on 6 df, p=0.004
## Wald test = 11 on 6 df, p=0.09
## Score (logrank) test = 16.4 on 6 df, p=0.01
EcogSP Everyday Language
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 231, number of events= 37
## (384 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 4.747e-02 1.049e+00 4.193e-01 0.113 0.910
## thirtile_PGSLow PGS -5.841e-02 9.433e-01 4.204e-01 -0.139 0.889
## age 7.784e-01 2.178e+00 3.459e+00 0.225 0.822
## sqrt(age) -1.274e+01 2.925e-06 5.826e+01 -0.219 0.827
## PTGENDERFemale 2.510e+00 1.231e+01 6.998e+00 0.359 0.720
## age:PTGENDERFemale -4.011e-02 9.607e-01 9.923e-02 -0.404 0.686
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.049e+00 9.536e-01 4.610e-01 2.385e+00
## thirtile_PGSLow PGS 9.433e-01 1.060e+00 4.138e-01 2.150e+00
## age 2.178e+00 4.591e-01 2.477e-03 1.915e+03
## sqrt(age) 2.925e-06 3.419e+05 7.546e-56 1.134e+44
## PTGENDERFemale 1.231e+01 8.125e-02 1.359e-05 1.114e+07
## age:PTGENDERFemale 9.607e-01 1.041e+00 7.909e-01 1.167e+00
##
## Concordance= 0.576 (se = 0.057 )
## Likelihood ratio test= 1.03 on 6 df, p=1
## Wald test = 1.01 on 6 df, p=1
## Score (logrank) test = 1.02 on 6 df, p=1## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 231, number of events= 37
## (384 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 7.830e-01 2.188e+00 4.909e-01 1.595 0.111
## thirtile_yearsHigh EA -1.595e-01 8.525e-01 3.854e-01 -0.414 0.679
## age -8.314e-01 4.355e-01 3.436e+00 -0.242 0.809
## sqrt(age) 1.381e+01 9.948e+05 5.782e+01 0.239 0.811
## PTGENDERFemale 3.608e+00 3.689e+01 7.149e+00 0.505 0.614
## age:PTGENDERFemale -5.845e-02 9.432e-01 1.014e-01 -0.576 0.564
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.188e+00 4.570e-01 8.360e-01 5.726e+00
## thirtile_yearsHigh EA 8.525e-01 1.173e+00 4.005e-01 1.815e+00
## age 4.355e-01 2.296e+00 5.177e-04 3.662e+02
## sqrt(age) 9.948e+05 1.005e-06 6.013e-44 1.646e+55
## PTGENDERFemale 3.689e+01 2.711e-02 3.030e-05 4.491e+07
## age:PTGENDERFemale 9.432e-01 1.060e+00 7.732e-01 1.151e+00
##
## Concordance= 0.649 (se = 0.052 )
## Likelihood ratio test= 4.56 on 6 df, p=0.6
## Wald test = 4.66 on 6 df, p=0.6
## Score (logrank) test = 4.7 on 6 df, p=0.6
EcogSP Everyday Memory
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 220, number of events= 31
## (384 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -2.555e-01 7.745e-01 3.881e-01 -0.658 0.51025
## thirtile_PGSLow PGS -1.828e+00 1.608e-01 6.471e-01 -2.824 0.00474 **
## age -1.262e+00 2.830e-01 3.828e+00 -0.330 0.74161
## sqrt(age) 2.376e+01 2.076e+10 6.445e+01 0.369 0.71243
## PTGENDERFemale 2.399e+01 2.618e+10 1.015e+01 2.363 0.01813 *
## age:PTGENDERFemale -3.444e-01 7.086e-01 1.457e-01 -2.364 0.01806 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 7.745e-01 1.291e+00 3.620e-01 1.657e+00
## thirtile_PGSLow PGS 1.608e-01 6.219e+00 4.523e-02 5.716e-01
## age 2.830e-01 3.533e+00 1.561e-04 5.132e+02
## sqrt(age) 2.076e+10 4.816e-11 2.854e-45 1.511e+65
## PTGENDERFemale 2.618e+10 3.819e-11 5.982e+01 1.146e+19
## age:PTGENDERFemale 7.086e-01 1.411e+00 5.326e-01 9.428e-01
##
## Concordance= 0.764 (se = 0.044 )
## Likelihood ratio test= 21.29 on 6 df, p=0.002
## Wald test = 16.01 on 6 df, p=0.01
## Score (logrank) test = 19.12 on 6 df, p=0.004## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 220, number of events= 31
## (384 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 8.917e-01 2.439e+00 6.190e-01 1.440 0.1497
## thirtile_yearsHigh EA 4.022e-01 1.495e+00 4.347e-01 0.925 0.3549
## age -3.602e+00 2.727e-02 4.237e+00 -0.850 0.3952
## sqrt(age) 6.159e+01 5.623e+26 7.119e+01 0.865 0.3869
## PTGENDERFemale 2.174e+01 2.772e+09 1.020e+01 2.132 0.0330 *
## age:PTGENDERFemale -3.140e-01 7.305e-01 1.464e-01 -2.145 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.439e+00 4.100e-01 7.250e-01 8.207e+00
## thirtile_yearsHigh EA 1.495e+00 6.688e-01 6.377e-01 3.505e+00
## age 2.727e-02 3.668e+01 6.749e-06 1.102e+02
## sqrt(age) 5.623e+26 1.778e-27 1.414e-34 2.237e+87
## PTGENDERFemale 2.772e+09 3.608e-10 5.759e+00 1.334e+18
## age:PTGENDERFemale 7.305e-01 1.369e+00 5.483e-01 9.732e-01
##
## Concordance= 0.678 (se = 0.055 )
## Likelihood ratio test= 11.92 on 6 df, p=0.06
## Wald test = 10.35 on 6 df, p=0.1
## Score (logrank) test = 11.55 on 6 df, p=0.07
EcogSP Everyday Organization
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 209, number of events= 18
## (391 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 1.054e-01 1.111e+00 5.391e-01 0.196 0.845
## thirtile_PGSLow PGS -1.294e+00 2.742e-01 8.290e-01 -1.561 0.119
## age 3.442e+00 3.125e+01 4.818e+00 0.714 0.475
## sqrt(age) -5.746e+01 1.110e-25 8.114e+01 -0.708 0.479
## PTGENDERFemale 1.592e+01 8.184e+06 1.300e+01 1.224 0.221
## age:PTGENDERFemale -2.293e-01 7.951e-01 1.869e-01 -1.227 0.220
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.111e+00 8.999e-01 3.863e-01 3.197e+00
## thirtile_PGSLow PGS 2.742e-01 3.647e+00 5.400e-02 1.392e+00
## age 3.125e+01 3.200e-02 2.475e-03 3.945e+05
## sqrt(age) 1.110e-25 9.012e+24 9.436e-95 1.305e+44
## PTGENDERFemale 8.184e+06 1.222e-07 7.020e-05 9.543e+17
## age:PTGENDERFemale 7.951e-01 1.258e+00 5.512e-01 1.147e+00
##
## Concordance= 0.749 (se = 0.063 )
## Likelihood ratio test= 15.86 on 6 df, p=0.01
## Wald test = 14.57 on 6 df, p=0.02
## Score (logrank) test = 18.49 on 6 df, p=0.005## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 209, number of events= 18
## (391 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 1.662e+00 5.272e+00 8.005e-01 2.077 0.0378 *
## thirtile_yearsHigh EA 3.247e-02 1.033e+00 5.818e-01 0.056 0.9555
## age -3.454e+00 3.163e-02 5.552e+00 -0.622 0.5339
## sqrt(age) 5.662e+01 3.907e+24 9.310e+01 0.608 0.5431
## PTGENDERFemale 1.784e+01 5.596e+07 1.358e+01 1.314 0.1889
## age:PTGENDERFemale -2.576e-01 7.729e-01 1.953e-01 -1.319 0.1872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 5.272e+00 1.897e-01 1.098e+00 2.531e+01
## thirtile_yearsHigh EA 1.033e+00 9.681e-01 3.303e-01 3.231e+00
## age 3.163e-02 3.162e+01 5.945e-07 1.683e+03
## sqrt(age) 3.907e+24 2.560e-25 2.202e-55 6.932e+103
## PTGENDERFemale 5.596e+07 1.787e-08 1.548e-04 2.022e+19
## age:PTGENDERFemale 7.729e-01 1.294e+00 5.270e-01 1.133e+00
##
## Concordance= 0.681 (se = 0.081 )
## Likelihood ratio test= 16.54 on 6 df, p=0.01
## Wald test = 17.59 on 6 df, p=0.007
## Score (logrank) test = 21.18 on 6 df, p=0.002
EcogSP Everyday Planning
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 215, number of events= 33
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -2.026e-01 8.166e-01 3.978e-01 -0.509 0.611
## thirtile_PGSLow PGS -1.058e+00 3.473e-01 5.096e-01 -2.075 0.038 *
## age 1.117e+00 3.056e+00 3.449e+00 0.324 0.746
## sqrt(age) -1.678e+01 5.146e-08 5.809e+01 -0.289 0.773
## PTGENDERFemale 1.251e+01 2.723e+05 8.120e+00 1.541 0.123
## age:PTGENDERFemale -1.782e-01 8.368e-01 1.153e-01 -1.545 0.122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.166e-01 1.225e+00 3.744e-01 1.781e+00
## thirtile_PGSLow PGS 3.473e-01 2.879e+00 1.279e-01 9.429e-01
## age 3.056e+00 3.272e-01 3.541e-03 2.638e+03
## sqrt(age) 5.146e-08 1.943e+07 1.853e-57 1.429e+42
## PTGENDERFemale 2.723e+05 3.672e-06 3.339e-02 2.221e+12
## age:PTGENDERFemale 8.368e-01 1.195e+00 6.675e-01 1.049e+00
##
## Concordance= 0.686 (se = 0.049 )
## Likelihood ratio test= 8.3 on 6 df, p=0.2
## Wald test = 7.62 on 6 df, p=0.3
## Score (logrank) test = 8 on 6 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 215, number of events= 33
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 8.306e-01 2.295e+00 5.507e-01 1.508 0.131
## thirtile_yearsHigh EA -1.017e-02 9.899e-01 4.050e-01 -0.025 0.980
## age -1.636e+00 1.947e-01 3.638e+00 -0.450 0.653
## sqrt(age) 2.834e+01 2.024e+12 6.115e+01 0.463 0.643
## PTGENDERFemale 1.215e+01 1.883e+05 8.200e+00 1.481 0.139
## age:PTGENDERFemale -1.742e-01 8.401e-01 1.164e-01 -1.497 0.134
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.295e+00 4.358e-01 7.799e-01 6.752e+00
## thirtile_yearsHigh EA 9.899e-01 1.010e+00 4.476e-01 2.189e+00
## age 1.947e-01 5.135e+00 1.560e-04 2.432e+02
## sqrt(age) 2.024e+12 4.940e-13 1.785e-40 2.296e+64
## PTGENDERFemale 1.883e+05 5.310e-06 1.974e-02 1.796e+12
## age:PTGENDERFemale 8.401e-01 1.190e+00 6.687e-01 1.055e+00
##
## Concordance= 0.591 (se = 0.057 )
## Likelihood ratio test= 5.73 on 6 df, p=0.5
## Wald test = 6.07 on 6 df, p=0.4
## Score (logrank) test = 6.26 on 6 df, p=0.4
EcogSP Everyday Visuospatial Abilities
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 211, number of events= 27
## (386 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS 4.585e-02 1.047e+00 4.357e-01 0.105 0.9162
## thirtile_PGSLow PGS -1.125e+00 3.246e-01 6.118e-01 -1.839 0.0659 .
## age 1.175e+00 3.240e+00 3.917e+00 0.300 0.7641
## sqrt(age) -1.882e+01 6.700e-09 6.601e+01 -0.285 0.7755
## PTGENDERFemale 1.525e+01 4.194e+06 9.961e+00 1.531 0.1258
## age:PTGENDERFemale -2.266e-01 7.972e-01 1.434e-01 -1.580 0.1140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 1.047e+00 9.552e-01 4.457e-01 2.459e+00
## thirtile_PGSLow PGS 3.246e-01 3.080e+00 9.786e-02 1.077e+00
## age 3.240e+00 3.087e-01 1.500e-03 6.998e+03
## sqrt(age) 6.700e-09 1.493e+08 4.372e-65 1.027e+48
## PTGENDERFemale 4.194e+06 2.384e-07 1.393e-02 1.263e+15
## age:PTGENDERFemale 7.972e-01 1.254e+00 6.019e-01 1.056e+00
##
## Concordance= 0.699 (se = 0.056 )
## Likelihood ratio test= 12.31 on 6 df, p=0.06
## Wald test = 10.14 on 6 df, p=0.1
## Score (logrank) test = 11.32 on 6 df, p=0.08## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 211, number of events= 27
## (386 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 2.237e-01 1.251e+00 6.847e-01 0.327 0.744
## thirtile_yearsHigh EA -2.270e-01 7.969e-01 4.314e-01 -0.526 0.599
## age -4.772e-01 6.205e-01 4.135e+00 -0.115 0.908
## sqrt(age) 7.903e+00 2.707e+03 6.957e+01 0.114 0.910
## PTGENDERFemale 1.384e+01 1.022e+06 9.930e+00 1.394 0.163
## age:PTGENDERFemale -2.061e-01 8.137e-01 1.432e-01 -1.440 0.150
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 1.251e+00 7.996e-01 3.269e-01 4.785e+00
## thirtile_yearsHigh EA 7.969e-01 1.255e+00 3.421e-01 1.856e+00
## age 6.205e-01 1.612e+00 1.876e-04 2.052e+03
## sqrt(age) 2.707e+03 3.695e-04 1.653e-56 4.431e+62
## PTGENDERFemale 1.022e+06 9.783e-07 3.608e-03 2.896e+14
## age:PTGENDERFemale 8.137e-01 1.229e+00 6.146e-01 1.077e+00
##
## Concordance= 0.647 (se = 0.063 )
## Likelihood ratio test= 7.77 on 6 df, p=0.3
## Wald test = 6.66 on 6 df, p=0.4
## Score (logrank) test = 7.44 on 6 df, p=0.3
EcogSP Total
## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_PGS +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 212, number of events= 32
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_PGSHigh PGS -1.656e-01 8.473e-01 3.959e-01 -0.418 0.6756
## thirtile_PGSLow PGS -1.164e+00 3.123e-01 5.438e-01 -2.140 0.0323 *
## age -9.608e-01 3.826e-01 3.706e+00 -0.259 0.7954
## sqrt(age) 1.821e+01 8.096e+07 6.243e+01 0.292 0.7705
## PTGENDERFemale 1.707e+01 2.602e+07 8.955e+00 1.907 0.0566 .
## age:PTGENDERFemale -2.454e-01 7.824e-01 1.278e-01 -1.920 0.0549 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_PGSHigh PGS 8.473e-01 1.180e+00 3.900e-01 1.841e+00
## thirtile_PGSLow PGS 3.123e-01 3.203e+00 1.076e-01 9.065e-01
## age 3.826e-01 2.614e+00 2.681e-04 5.459e+02
## sqrt(age) 8.096e+07 1.235e-08 5.808e-46 1.129e+61
## PTGENDERFemale 2.602e+07 3.844e-08 6.210e-01 1.090e+15
## age:PTGENDERFemale 7.824e-01 1.278e+00 6.090e-01 1.005e+00
##
## Concordance= 0.683 (se = 0.049 )
## Likelihood ratio test= 10.14 on 6 df, p=0.1
## Wald test = 8.64 on 6 df, p=0.2
## Score (logrank) test = 9.22 on 6 df, p=0.2## Call:
## coxph(formula = Surv(tstart, tstop, get(event)) ~ thirtile_years +
## age + sqrt(age) + PTGENDER + age * PTGENDER, data = filtered_data_frame)
##
## n= 212, number of events= 32
## (385 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## thirtile_yearsLow EA 8.064e-01 2.240e+00 5.774e-01 1.397 0.1625
## thirtile_yearsHigh EA 1.040e-02 1.010e+00 4.054e-01 0.026 0.9795
## age -3.503e+00 3.010e-02 3.908e+00 -0.896 0.3700
## sqrt(age) 5.963e+01 7.902e+25 6.571e+01 0.908 0.3641
## PTGENDERFemale 1.589e+01 7.993e+06 8.980e+00 1.770 0.0767 .
## age:PTGENDERFemale -2.302e-01 7.944e-01 1.282e-01 -1.796 0.0725 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## thirtile_yearsLow EA 2.240e+00 4.464e-01 7.223e-01 6.946e+00
## thirtile_yearsHigh EA 1.010e+00 9.897e-01 4.565e-01 2.237e+00
## age 3.010e-02 3.322e+01 1.420e-05 6.382e+01
## sqrt(age) 7.902e+25 1.266e-26 9.284e-31 6.726e+81
## PTGENDERFemale 7.993e+06 1.251e-07 1.814e-01 3.522e+14
## age:PTGENDERFemale 7.944e-01 1.259e+00 6.179e-01 1.021e+00
##
## Concordance= 0.633 (se = 0.058 )
## Likelihood ratio test= 6.52 on 6 df, p=0.4
## Wald test = 6.02 on 6 df, p=0.4
## Score (logrank) test = 6.18 on 6 df, p=0.4
Print session info:
## R version 4.3.0 (2023-04-21 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22621)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_Belgium.utf8 LC_CTYPE=English_Belgium.utf8
## [3] LC_MONETARY=English_Belgium.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Belgium.utf8
##
## time zone: Europe/Brussels
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survminer_0.4.9 ggpubr_0.6.0 ggplot2_3.4.2 rmdformats_1.0.4
## [5] gridExtra_2.3 NormPsy_1.0.8 survival_3.5-5 dplyr_1.1.2
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.3 xfun_0.39 bslib_0.5.0 rstatix_0.7.2
## [5] lattice_0.21-8 vctrs_0.6.2 tools_4.3.0 generics_0.1.3
## [9] parallel_4.3.0 tibble_3.2.1 fansi_1.0.4 highr_0.10
## [13] pkgconfig_2.0.3 Matrix_1.6-1.1 data.table_1.14.8 lifecycle_1.0.3
## [17] farver_2.1.1 compiler_4.3.0 munsell_0.5.0 codetools_0.2-19
## [21] carData_3.0-5 htmltools_0.5.5 sass_0.4.6 yaml_2.3.7
## [25] crayon_1.5.2 pillar_1.9.0 car_3.1-2 jquerylib_0.1.4
## [29] tidyr_1.3.0 randtoolbox_2.0.4 cachem_1.0.8 iterators_1.0.14
## [33] abind_1.4-5 foreach_1.5.2 nlme_3.1-162 km.ci_0.5-6
## [37] lcmm_2.0.2 tidyselect_1.2.0 digest_0.6.31 mvtnorm_1.2-2
## [41] purrr_1.0.1 bookdown_0.35 labeling_0.4.2 splines_4.3.0
## [45] fastmap_1.1.1 grid_4.3.0 colorspace_2.1-0 cli_3.6.1
## [49] magrittr_2.0.3 utf8_1.2.3 broom_1.0.5 withr_2.5.0
## [53] scales_1.2.1 backports_1.4.1 rmarkdown_2.22 rngWELL_0.10-9
## [57] ggsignif_0.6.4 zoo_1.8-12 evaluate_0.21 knitr_1.43
## [61] KMsurv_0.1-5 doParallel_1.0.17 mgcv_1.8-42 survMisc_0.5.6
## [65] rlang_1.1.1 xtable_1.8-4 glue_1.6.2 rstudioapi_0.14
## [69] jsonlite_1.8.7 R6_2.5.1 marqLevAlg_2.0.8